上海海洋大学学报2026，Vol.35Issue(4)：855-866,12.DOI:10.12024/jsou.20260405090
基于Hybrid RAG的LLM水产营养推荐架构
Hybrid RAG-Based LLM architecture for aquaculture nutrition recommendation
摘要
Abstract
To overcome the fragmentation of information in aquatic nutrition and the non-factual responses generated by large language models,this study proposes a framework for an aquatic nutrition recommendation system that integrates semantic vectorization with knowledge graph reasoning.Using DeepSeek,entities and relationships were automatically extracted from more than 60 papers related to aquatic nutrition,leading to the construction of a specialized knowledge graph containing 4 936 nodes and 4 515 edges.This graph not only represents the basic associations between aquatic ingredients and nutritional components,but also establishes safety-oriented dietary contraindication chains for metabolic diseases such as gout and diabetes.At the service layer,the system adopts a dual-engine architecture by combining a vector database for broad semantic matching with a graph database capable of multi-hop reasoning.This design helps overcome the limitations of traditional single-point query methods.The experimental results show that the proposed hybrid information retrieval system can maintain the accuracy of recommendation results while enhancing the evidence-based medical support of the generated content.Empirical findings further indicate that the integration of semantic representation and knowledge graph reasoning improves the accuracy and credibility of nutrition plans for aquatic products.Overall,this study provides technical and data support for the development of aquatic nutrition regulation systems and offers a useful reference for related research.关键词
水产营养/大语言模型/混合RAG/知识图谱/语义向量Key words
aquatic nutrition/large language model(LLM)/Hybrid RAG/knowledge graph/semantic vector分类
农业科技引用本文复制引用
高佳,艾新宇,张胜茂..基于Hybrid RAG的LLM水产营养推荐架构[J].上海海洋大学学报,2026,35(4):855-866,12.基金项目
中国水产科学研究院东海水产研究所中央级公益性科研院所基本科研业务费专项(2024TD04) （2024TD04）
